# Anatomical Characteristics Predict Response to Transcranial Direct Current Stimulation (tDCS): Development of a Computational Pipeline for Optimizing tDCS Protocols

## TL;DR

This study shows how anatomical differences affect tDCS effectiveness and proposes a computational method to personalize treatment protocols.

## Contribution

A computational pipeline is developed to optimize tDCS protocols by incorporating anatomical variables and predicting individual responsiveness.

## Key findings

- Anatomical variables like CSF volume and skull thickness significantly correlate with electric field distribution (p < 0.05).
- Brain tissue exposed to electric fields above 0.227 V/m is a key factor in tDCS responsiveness.
- Multiple regression models can predict individual responsiveness and guide personalized tDCS protocols.

## Abstract

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique promisingly used to treat neurological and psychological disorders. Nevertheless, the inter-subject heterogeneity in its after-effects frequently limits its efficacy. This can be attributed to fixed-dose methods, which do not consider inter-subject anatomical variations. This work attempts to overcome this constraint by examining the effects of age and anatomical features, including the volume of cerebrospinal fluid (CSF), the thickness of the skull, and the composition of brain tissue, on electric field distribution and cortical excitability. A computational approach was used to map the electric field distribution over the brain tissues of realistic head models reconstructed from MRI images of twenty-three subjects, including adults and children of both genders. Significant negative correlations (p < 0.05) were found in the data between the maximum electric field strength and anatomical variable parameters. Furthermore, this study showed that the percentage of brain tissue exposed to an electric field amplitude above a pre-defined threshold (i.e., 0.227 V/m) was the main factor influencing the responsiveness to tDCS. In the end, the research suggests multiple regression models as useful tool to predict subjects’ responsiveness and to support a personalized approach that tailors the injected current to the morphology of the patient.